Coordinate transformations play a pivotal role in computer graphics, robotics, and the broader domains of mathematics and physics. This project introduces a novel computable coordinate system object that bridges the gap between theoretical frameworks and numerical computations.

SPDlearn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization

A Unity HLSL implementation of parallax occlusion mapping with self-shadowing. Includes support for steep parallax, self-shadowing, normal mapping, roughness and metallic surfaces— all rendered in the Built-in Render Pipeline.

[NeurIPS 2024] Reproducible code for our article "Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems"

Conceptual path finding in high-dimensional embedding space via Grassmann manifold geometry. Replaces cosine similarity with tangent space distance — paths follow domain-coherent lanes instead of converging to vocabulary hubs. GPU-accelerated Julia compute worker